Applied Statistics with R: A Practical Guide for the Life Sciences
by: Justin C. Touchon
Publisher Finelybook 出版社：OUP Oxford (1 July 2021)
pages 页数：336 pages
The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced. Whether at the undergraduate, graduate, or post-graduate level, this book provides the tools needed to properly analyze your data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner, ensuring that readers come away with the knowledge of which analyses they should use and when they should use them.
The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the ‘go-to’ stats program throughout the life-sciences. Furthermore, by using a single, real-world dataset throughout the book, readers are encouraged to become deeply familiar with an imperfect but realistic set of data. Indeed, early chapters are specifically designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions.
Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.
1: Introduction to r
2: Before You Begin(aka Thoughts on Proper Data Analysis
3: Exploratory Data Analysis and Data Summarization
4: Introduction to Plotting
5: Basic Statistical Analyses
6: More Linear models
7: Generalized Linear Models(GLM)
8: Mixed Effects models
9: Advanced Data Wrangling and plotting
10: Writing Loops and Functions in R
11: Final Thoughts